As a method for image a spatial distribution of a gamma ray source, there is proposed a method of using a Compton camera (see, for example, Patent Document 1 below). The Compton camera has a wider imaging field of view and a wider detected energy band than positron emission tomography (PET) and single photon emission computed tomography (SPECT), which are existing nuclear medicine technology. The Compton camera enables not only an improvement of the existing technology but also multiple molecular simultaneous imaging for imaging various types of radionuclides at the same time, which was conventionally difficult. This is a technology that enables non-invasive visualization of behaviors of a plurality of radioactive diagnostic pharmaceuticals and biofunctional molecules, so that biological functional information can be obtained at higher level than conventional techniques. Therefore, the Compton camera is expected to contribute to life science research, early diagnosis in clinical medicine, and the like.
A typical Compton camera includes two radiation detectors as a first-stage detector and a second-stage detector, which can measure interaction position and energy. An imaging device using the Compton camera focuses on a gamma ray detection phenomenon in which gamma rays from a gamma ray source are Compton scattered by the first-stage detector and then photoelectrically absorbed by the second-stage detector, and the imaging device images a spatial distribution of the gamma ray source based on interaction information of each detector and gamma rays (namely a detected position and detected energy of gamma rays in each detector).
As a method for imaging the spatial distribution of the gamma ray source, there is proposed an image reconstruction method based on a list-mode maximum-likelihood expectation-maximization (LM-ML-EM) method. This is a method that enables to determine the gamma ray source distribution so that an expected value of likelihood is maximized by iterative calculation even if it is difficult to determine the gamma ray source distribution in the space from measurement data of observed gamma rays by maximum likelihood estimation.
A three-dimensional image indicating the spatial distribution of the gamma ray source is referred to as a distribution image. In a typical image reconstruction method using the LM-ML-EM method, a pixel value λj of a pixel j in the distribution image is determined in accordance with the following equation (1) (see, for example, Non-Patent Document 1 below). In the LM-ML-EM method, the pixel value is updated by the iterative calculation, so that the estimated distribution of the gamma ray source becomes close to a real distribution. Symbols λj(l) and λj(l+1) represent pixel values of the pixel j obtained by the iterative calculation at l-th time and (l+1)th time, respectively. Symbol sj represents a detection sensitivity parameter for a pixel j and is constituted of a geometrical efficiency of the first-stage detector viewed from the pixel j, and the like. Division by the detection sensitivity parameter sj means correction of the pixel value by taking the detection sensitivity into consideration. Meanings of other parameters in the equation (1) will be described later in detail.
                    [                  Mathematical          ⁢                                          ⁢          1                ]                                                                      λ          j                      (                          l              +              1                        )                          =                                            λ              j                              (                l                )                                                    s              j                                ⁢                                    ∑              i                        ⁢                                                  ⁢                                                            Y                  i                                ⁢                                  t                  ij                                                                              ∑                  k                                ⁢                                                      t                    ik                                    ⁢                                      λ                    k                                          (                      l                      )                                                                                                                              (        1        )            